Overview

Dataset statistics

Number of variables25
Number of observations1000
Missing cells100
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.6 KiB
Average record size in memory77.5 B

Variable types

Numeric7
Categorical18

Alerts

C4 is highly correlated with C9 and 1 other fieldsHigh correlation
C9 is highly correlated with C4 and 1 other fieldsHigh correlation
C25 is highly correlated with C4 and 1 other fieldsHigh correlation
C4 is highly correlated with C9 and 1 other fieldsHigh correlation
C9 is highly correlated with C4 and 1 other fieldsHigh correlation
C25 is highly correlated with C4 and 1 other fieldsHigh correlation
C4 is highly correlated with C25High correlation
C25 is highly correlated with C4High correlation
C24 is highly correlated with C8High correlation
C8 is highly correlated with C24High correlation
Class is highly correlated with C7High correlation
C4 is highly correlated with C9 and 1 other fieldsHigh correlation
C7 is highly correlated with ClassHigh correlation
C8 is highly correlated with C24High correlation
C9 is highly correlated with C4 and 1 other fieldsHigh correlation
C14 is highly correlated with C22High correlation
C22 is highly correlated with C14High correlation
C24 is highly correlated with C8High correlation
C25 is highly correlated with C4 and 1 other fieldsHigh correlation
Class has 100 (10.0%) missing values Missing
df_index has unique values Unique

Reproduction

Analysis started2021-10-02 08:20:09.991711
Analysis finished2021-10-02 08:20:21.800751
Duration11.81 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean509.5
Minimum0
Maximum1099
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:21.880500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.95
Q1249.75
median499.5
Q3749.25
95-th percentile1049.05
Maximum1099
Range1099
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation305.4939878
Coefficient of variation (CV)0.5995956581
Kurtosis-1.002572921
Mean509.5
Median Absolute Deviation (MAD)250
Skewness0.167175411
Sum509500
Variance93326.57658
MonotonicityStrictly increasing
2021-10-02T16:20:21.991536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
0.1%
6711
 
0.1%
6581
 
0.1%
6591
 
0.1%
6601
 
0.1%
6611
 
0.1%
6621
 
0.1%
6631
 
0.1%
6641
 
0.1%
6651
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
10991
0.1%
10981
0.1%
10971
0.1%
10961
0.1%
10951
0.1%
10941
0.1%
10931
0.1%
10921
0.1%
10911
0.1%
10901
0.1%

Class
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing100
Missing (%)10.0%
Memory size1.2 KiB
0.0
650 
1.0
250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0650
65.0%
1.0250
 
25.0%
(Missing)100
 
10.0%

Length

2021-10-02T16:20:22.182498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:22.258499image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0650
72.2%
1.0250
 
27.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C1
Real number (ℝ≥0)

Distinct55
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.039
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:22.347594image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q126
median32
Q341
95-th percentile59
Maximum75
Range57
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.39857037
Coefficient of variation (CV)0.3253109497
Kurtosis0.568088875
Mean35.039
Median Absolute Deviation (MAD)7
Skewness1.008867955
Sum35039
Variance129.9274064
MonotonicityNot monotonic
2021-10-02T16:20:22.451611image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2654
 
5.4%
2749
 
4.9%
2548
 
4.8%
2342
 
4.2%
2940
 
4.0%
3239
 
3.9%
2439
 
3.9%
3539
 
3.9%
2239
 
3.9%
3636
 
3.6%
Other values (45)575
57.5%
ValueCountFrequency (%)
182
 
0.2%
195
 
0.5%
2016
 
1.6%
2124
2.4%
2239
3.9%
2342
4.2%
2439
3.9%
2548
4.8%
2654
5.4%
2749
4.9%
ValueCountFrequency (%)
751
 
0.1%
743
0.3%
732
 
0.2%
701
 
0.1%
681
 
0.1%
673
0.3%
664
0.4%
657
0.7%
643
0.3%
635
0.5%

C2
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
yes
963 
no
 
37

Length

Max length3
Median length3
Mean length2.963
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes

Common Values

ValueCountFrequency (%)
yes963
96.3%
no37
 
3.7%

Length

2021-10-02T16:20:22.552656image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:22.606623image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
yes963
96.3%
no37
 
3.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C3
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V3
345 
V5
251 
V2
171 
V4
171 
V1
62 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV5
2nd rowV3
3rd rowV5
4th rowV3
5th rowV3

Common Values

ValueCountFrequency (%)
V3345
34.5%
V5251
25.1%
V2171
17.1%
V4171
17.1%
V162
 
6.2%

Length

2021-10-02T16:20:22.662606image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:22.718556image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v3345
34.5%
v5251
25.1%
v2171
17.1%
v4171
17.1%
v162
 
6.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C4
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.46443367
Minimum3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:22.796603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q111
median18
Q324
95-th percentile47
Maximum72
Range69
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.06211599
Coefficient of variation (CV)0.5894185094
Kurtosis0.9382521302
Mean20.46443367
Median Absolute Deviation (MAD)6.5
Skewness1.098691202
Sum20464.43367
Variance145.4946421
MonotonicityNot monotonic
2021-10-02T16:20:22.901555image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1199
 
9.9%
2493
 
9.3%
2389
 
8.9%
1287
 
8.7%
1760
 
6.0%
1852
 
5.2%
3649
 
4.9%
941
 
4.1%
639
 
3.9%
1439
 
3.9%
Other values (36)352
35.2%
ValueCountFrequency (%)
33
 
0.3%
43
 
0.3%
536
 
3.6%
639
 
3.9%
74
 
0.4%
827
 
2.7%
941
4.1%
1018
 
1.8%
1199
9.9%
1287
8.7%
ValueCountFrequency (%)
721
 
0.1%
609
 
0.9%
594
 
0.4%
541
 
0.1%
531
 
0.1%
4821
2.1%
4727
2.7%
461
 
0.1%
454
 
0.4%
441
 
0.1%

C5
Categorical

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
V3
280 
V7
234 
V2
181 
V1
103 
V9
97 
Other values (5)
105 

Length

Max length3
Median length2
Mean length2.012
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV2
2nd rowV7
3rd rowV2
4th rowV7
5th rowV1

Common Values

ValueCountFrequency (%)
V3280
28.0%
V7234
23.4%
V2181
18.1%
V1103
 
10.3%
V997
 
9.7%
V650
 
5.0%
V522
 
2.2%
V1012
 
1.2%
V412
 
1.2%
V89
 
0.9%

Length

2021-10-02T16:20:23.000664image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:23.062603image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v3280
28.0%
v7234
23.4%
v2181
18.1%
v1103
 
10.3%
v997
 
9.7%
v650
 
5.0%
v522
 
2.2%
v1012
 
1.2%
v412
 
1.2%
v89
 
0.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C6
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V1
603 
V5
183 
V2
103 
V3
63 
V4
 
48

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV1
2nd rowV1
3rd rowV1
4th rowV1
5th rowV1

Common Values

ValueCountFrequency (%)
V1603
60.3%
V5183
 
18.3%
V2103
 
10.3%
V363
 
6.3%
V448
 
4.8%

Length

2021-10-02T16:20:23.152600image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:23.207596image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v1603
60.3%
v5183
 
18.3%
v2103
 
10.3%
v363
 
6.3%
v448
 
4.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C7
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V4
394 
V1
274 
V2
269 
V3
63 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV1
2nd rowV1
3rd rowV2
4th rowV1
5th rowV4

Common Values

ValueCountFrequency (%)
V4394
39.4%
V1274
27.4%
V2269
26.9%
V363
 
6.3%

Length

2021-10-02T16:20:23.275501image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:23.329593image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v4394
39.4%
v1274
27.4%
v2269
26.9%
v363
 
6.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C8
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V4
332 
V1
282 
V2
232 
V3
154 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV3
2nd rowV1
3rd rowV3
4th rowV4
5th rowV1

Common Values

ValueCountFrequency (%)
V4332
33.2%
V1282
28.2%
V2232
23.2%
V3154
15.4%

Length

2021-10-02T16:20:23.394790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:23.447792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v4332
33.2%
v1282
28.2%
v2232
23.2%
v3154
15.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C9
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct916
Distinct (%)91.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3270.739
Minimum249
Maximum18424
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:23.522792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum249
5-th percentile708.9
Q11365.5
median2319
Q33971.25
95-th percentile9162.65
Maximum18424
Range18175
Interquartile range (IQR)2605.75

Descriptive statistics

Standard deviation2822.732086
Coefficient of variation (CV)0.8630257827
Kurtosis4.292731863
Mean3270.739
Median Absolute Deviation (MAD)1098
Skewness1.949655282
Sum3270739
Variance7967816.427
MonotonicityNot monotonic
2021-10-02T16:20:23.628792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12573
 
0.3%
15333
 
0.3%
12163
 
0.3%
33982
 
0.2%
10372
 
0.2%
22462
 
0.2%
13812
 
0.2%
7012
 
0.2%
31482
 
0.2%
12642
 
0.2%
Other values (906)977
97.7%
ValueCountFrequency (%)
2491
0.1%
2761
0.1%
3382
0.2%
3431
0.1%
3621
0.1%
3671
0.1%
3851
0.1%
3921
0.1%
4081
0.1%
4251
0.1%
ValueCountFrequency (%)
184241
0.1%
159441
0.1%
158571
0.1%
156711
0.1%
156521
0.1%
148951
0.1%
147821
0.1%
145541
0.1%
144211
0.1%
143171
0.1%

C13
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
V3
816 
V1
137 
V2
 
47

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV3
2nd rowV3
3rd rowV3
4th rowV3
5th rowV3

Common Values

ValueCountFrequency (%)
V3816
81.6%
V1137
 
13.7%
V247
 
4.7%

Length

2021-10-02T16:20:23.814321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:23.868363image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v3816
81.6%
v1137
 
13.7%
v247
 
4.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C14
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V3
630 
V2
200 
V4
148 
V1
 
22

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV4
2nd rowV3
3rd rowV4
4th rowV3
5th rowV3

Common Values

ValueCountFrequency (%)
V3630
63.0%
V2200
 
20.0%
V4148
 
14.8%
V122
 
2.2%

Length

2021-10-02T16:20:23.930321image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:23.980320image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v3630
63.0%
v2200
 
20.0%
v4148
 
14.8%
v122
 
2.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C16
Real number (ℝ≥0)

Distinct996
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40647.645
Minimum1446
Maximum220716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:24.058395image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1446
5-th percentile7784.6
Q119447.75
median33797
Q356198.75
95-th percentile96787.8
Maximum220716
Range219270
Interquartile range (IQR)36751

Descriptive statistics

Standard deviation28281.59232
Coefficient of variation (CV)0.6957744371
Kurtosis2.802524804
Mean40647.645
Median Absolute Deviation (MAD)16987.5
Skewness1.340223268
Sum40647645
Variance799848464.1
MonotonicityNot monotonic
2021-10-02T16:20:24.163107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
435602
 
0.2%
215402
 
0.2%
198362
 
0.2%
292722
 
0.2%
309641
 
0.1%
120811
 
0.1%
604121
 
0.1%
525901
 
0.1%
232251
 
0.1%
70671
 
0.1%
Other values (986)986
98.6%
ValueCountFrequency (%)
14461
0.1%
15601
0.1%
19001
0.1%
20621
0.1%
22961
0.1%
26681
0.1%
34571
0.1%
35461
0.1%
40011
0.1%
40301
0.1%
ValueCountFrequency (%)
2207161
0.1%
1872041
0.1%
1574351
0.1%
1551951
0.1%
1464391
0.1%
1453411
0.1%
1311071
0.1%
1271131
0.1%
1262331
0.1%
1252181
0.1%

C18
Categorical

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
V1
907 
V3
 
52
V2
 
41

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV1
2nd rowV1
3rd rowV1
4th rowV1
5th rowV1

Common Values

ValueCountFrequency (%)
V1907
90.7%
V352
 
5.2%
V241
 
4.1%

Length

2021-10-02T16:20:24.258787image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:24.309136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v1907
90.7%
v352
 
5.2%
v241
 
4.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C19
Real number (ℝ≥0)

Distinct856
Distinct (%)85.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4999.929
Minimum2272
Maximum8633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:24.379284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum2272
5-th percentile3405.6
Q14322.75
median4976.5
Q35699.5
95-th percentile6687.9
Maximum8633
Range6361
Interquartile range (IQR)1376.75

Descriptive statistics

Standard deviation1011.98354
Coefficient of variation (CV)0.202399582
Kurtosis-0.1800648176
Mean4999.929
Median Absolute Deviation (MAD)676.5
Skewness0.1527404825
Sum4999929
Variance1024110.685
MonotonicityNot monotonic
2021-10-02T16:20:24.491614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44074
 
0.4%
55144
 
0.4%
56343
 
0.3%
53253
 
0.3%
42003
 
0.3%
49783
 
0.3%
46283
 
0.3%
56023
 
0.3%
36013
 
0.3%
56773
 
0.3%
Other values (846)968
96.8%
ValueCountFrequency (%)
22721
0.1%
22851
0.1%
25361
0.1%
25551
0.1%
25871
0.1%
26091
0.1%
26141
0.1%
26341
0.1%
26441
0.1%
26751
0.1%
ValueCountFrequency (%)
86331
0.1%
80661
0.1%
78051
0.1%
76151
0.1%
76091
0.1%
76051
0.1%
75951
0.1%
74531
0.1%
74371
0.1%
74291
0.1%

C20
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
4
413 
2
308 
3
149 
1
130 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4413
41.3%
2308
30.8%
3149
 
14.9%
1130
 
13.0%

Length

2021-10-02T16:20:24.590614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:24.644614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4413
41.3%
2308
30.8%
3149
 
14.9%
1130
 
13.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C21
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V2
530 
V4
293 
V3
88 
V1
 
49
V5
 
40

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV2
2nd rowV4
3rd rowV3
4th rowV2
5th rowV3

Common Values

ValueCountFrequency (%)
V2530
53.0%
V4293
29.3%
V388
 
8.8%
V149
 
4.9%
V540
 
4.0%

Length

2021-10-02T16:20:24.704782image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:24.757784image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v2530
53.0%
v4293
29.3%
v388
 
8.8%
v149
 
4.9%
v540
 
4.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C22
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
V1
596 
V2
404 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV2
2nd rowV1
3rd rowV2
4th rowV1
5th rowV1

Common Values

ValueCountFrequency (%)
V1596
59.6%
V2404
40.4%

Length

2021-10-02T16:20:24.824789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:24.876790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v1596
59.6%
v2404
40.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C23
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
1
633 
2
333 
3
 
28
4
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1633
63.3%
2333
33.3%
328
 
2.8%
46
 
0.6%

Length

2021-10-02T16:20:24.931837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:24.985837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1633
63.3%
2333
33.3%
328
 
2.8%
46
 
0.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C24
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
V2
713 
V1
179 
V3
108 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV3
2nd rowV1
3rd rowV2
4th rowV1
5th rowV1

Common Values

ValueCountFrequency (%)
V2713
71.3%
V1179
 
17.9%
V3108
 
10.8%

Length

2021-10-02T16:20:25.048792image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:25.102841image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v2713
71.3%
v1179
 
17.9%
v3108
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C25
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.421
Minimum3
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2021-10-02T16:20:25.175786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q111
median18
Q324
95-th percentile47
Maximum72
Range69
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.09255924
Coefficient of variation (CV)0.5921629323
Kurtosis0.922454624
Mean20.421
Median Absolute Deviation (MAD)7
Skewness1.094927827
Sum20421
Variance146.229989
MonotonicityNot monotonic
2021-10-02T16:20:25.287079image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1199
 
9.9%
2493
 
9.3%
2391
 
9.1%
1287
 
8.7%
1760
 
6.0%
1853
 
5.3%
3649
 
4.9%
941
 
4.1%
1439
 
3.9%
639
 
3.9%
Other values (35)349
34.9%
ValueCountFrequency (%)
33
 
0.3%
43
 
0.3%
539
 
3.9%
639
 
3.9%
74
 
0.4%
827
 
2.7%
941
4.1%
1018
 
1.8%
1199
9.9%
1287
8.7%
ValueCountFrequency (%)
721
 
0.1%
609
 
0.9%
594
 
0.4%
541
 
0.1%
531
 
0.1%
4821
2.1%
4727
2.7%
461
 
0.1%
454
 
0.4%
441
 
0.1%

C27
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
4
476 
2
231 
3
157 
1
136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row4
4th row2
5th row4

Common Values

ValueCountFrequency (%)
4476
47.6%
2231
23.1%
3157
 
15.7%
1136
 
13.6%

Length

2021-10-02T16:20:25.492081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:25.548078image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
4476
47.6%
2231
23.1%
3157
 
15.7%
1136
 
13.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C28
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
V3
548 
V2
310 
V4
92 
V1
 
50

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV3
2nd rowV3
3rd rowV1
4th rowV2
5th rowV4

Common Values

ValueCountFrequency (%)
V3548
54.8%
V2310
31.0%
V492
 
9.2%
V150
 
5.0%

Length

2021-10-02T16:20:25.616086image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:25.671136image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
v3548
54.8%
v2310
31.0%
v492
 
9.2%
v150
 
5.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

C29
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
1.0
848 
2.0
152 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0848
84.8%
2.0152
 
15.2%

Length

2021-10-02T16:20:25.736285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-02T16:20:25.784285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0848
84.8%
2.0152
 
15.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-02T16:20:20.299969image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:15.237004image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.161449image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.977834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.931918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.743917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.468918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.403916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:15.359786image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.264832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.087834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.078918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.847917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.665916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.507915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:15.470791image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.373831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.189834image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.184917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.946917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.761916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.634926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:15.617451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.481830image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.296972image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.292917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.047917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.865922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.745915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:15.767454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.587831image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.514919image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.402922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.150917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.979916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.844925image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:15.909451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.771835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.681926image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.510922image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.245916image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.085915image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.948966image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.050450image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:16.871835image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:17.819918image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:18.620917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:19.362917image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-10-02T16:20:20.191936image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-10-02T16:20:25.839285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-02T16:20:25.971284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-02T16:20:26.116284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-02T16:20:26.271284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-02T16:20:26.488352image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-02T16:20:21.175702image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-02T16:20:21.548877image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-02T16:20:21.692189image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexClassC1C2C3C4C5C6C7C8C9C13C14C16C18C19C20C21C22C23C24C25C27C28C29
001.053yesV511.0V2V1V1V37865V3V430964V138244V2V21V3114V31.0
110.035yesV311.0V7V1V1V13904V3V3157435V151602V4V12V1112V32.0
221.040yesV518.0V2V1V2V34296V3V421999V137203V3V21V2184V11.0
330.028yesV314.0V7V1V1V41402V3V356353V162454V2V11V1142V21.0
440.040yesV311.0V1V1V4V11503V3V36160V154964V3V11V1114V41.0
550.025yesV211.0V3V1V2V1624V1V213282V340301V2V11V2114V41.0
660.061yesV36.0V4V3V4V11337V3V358376V143044V2V11V261V11.0
771.024yesV212.0V2V1V2V22968V3V369444V159683V5V12V1124V21.0
880.025yesV311.0V2V1V4V1762V3V325653V171891V2V21V2114V21.0
990.044yesV536.0V7V1V4V410874V3V313784V143992V3V22V2362V32.0

Last rows

df_indexClassC1C2C3C4C5C6C7C8C9C13C14C16C18C19C20C21C22C23C24C25C27C28C29
9901090NaN43yesV217.0V2V1V4V21533V3V244754V244871V2V11V2174V42.0
9911091NaN25yesV318.0V3V1V1V11344V1V330397V173703V2V11V2184V41.0
9921092NaN29yesV521.0V7V1V1V41601V3V325331V158413V4V22V2214V41.0
9931093NaN46yesV524.0V7V1V4V42538V3V237402V133454V3V12V2244V32.0
9941094NaN22yesV213.0V3V1V2V22100V3V214812V361164V2V11V2132V21.0
9951095NaN31yesV348.0V3V1V1V46758V3V323786V142002V2V21V2483V21.0
9961096NaN39yesV522.0V3V3V4V42674V3V343446V148444V2V11V2223V31.0
9971097NaN23yesV317.0V2V1V1V12123V3V318760V140444V4V12V1174V21.0
9981098NaN25noV25.0V3V1V2V1589V3V235026V150543V2V11V253V41.0
9991099NaN34yesV37.0V3V1V2V12576V3V366466V350342V2V11V272V31.0